T cell subpopulations capable of treating cancer

ABSTRACT

A method of determining responsiveness to cancer treatment is disclosed. The method comprises analyzing a frequency of tumor infiltrating lymphocytes (TILs) having a CD8 + CD28 − CD152 −  signature in a sample of the subject, wherein a frequency of TILs having the CD8 + CD28 − CD152 −  signature above a predetermined level is indicative of a positive responsiveness to cancer treatment. Other signatures reflecting responsiveness to cancer treatment are also disclosed. In addition, methods of treating cancer based on these signatures are also disclosed.

RELATED APPLICATION

The teachings of U.S. Provisional Patent Application No. 60/960,291filed on Sep. 24, 2007 are incorporated by reference as if fully setforth herein.

FIELD AND BACKGROUND OF THE INVENTION

The present invention, in some embodiments thereof, relates to T cellpopulations capable of treating cancer. Current therapeutic strategiesfocus predominantly on achieving the removal or death of cancer cellswithin the patient, through a diverse array of surgical and non-surgicaltechniques; the most widely used are chemotherapy and gamma irradiation.Those methods have a number of prominent disadvantages, in particularthe culling of healthy cells/tissues within the patient, and the toxicside-effects of the current generation of chemotherapeutic drugsutilized in cancer treatment. Furthermore, these treatments are notalways successful.

The spontaneous regression of certain cancers, such as melanoma or renalcell cancer, supports the idea that the immune system is sometimescapable of delaying tumor progression and on rare occasions eliminatinga tumor altogether.

These observations have led to research interest in a variety ofimmunologic therapies designed to stimulate the immune system.

Further evidence that an immune response to cancer exists in humans isprovided by the existence of lymphocytes within melanoma deposits. Theselymphocytes, when isolated, are capable of recognizing specific tumorantigens on autologous and allogeneic melanomas in an MHC restrictedfashion. Tumor infiltrating lymphocytes (TILs) from patients withmetastatic melanoma recognize shared antigens includingmelanocyte-melanoma lineage specific tissue antigens in vitro (Kawakami,Y., et al., (1993) J. Immunother. 14: 88-93; Anichini, A. et al., (1993)et al., J. Exp. Med. 177: 989-998). Anti-melanoma T cells appear to beenriched in TILs probably as a consequence of clonal expansion andaccumulation at the tumor site in vivo (Sensi, M., et al., (1993) J.Exp. Med. 178:1231-1246).

The term adoptive immunotherapy describes the transfer ofimmunocompetent cells such as the TILs described herein above to thetumor-bearing host. Adoptive cell transfer (ACT) therapy for patientswith cancer relies on the ex vivo generation of highly active tumor,specific lymphocytes, and their administration in large numbers to theautologous host.

Presently, ACT therapy however effectively treats only a limited numberof patients. Preclinical models have identified a variety of ways tomanipulate the host immune environment that increase ACT therapeuticefficacy. These include immunosuppression prior to cell administrationand concurrent interleukin 2 administration with the transferred Tcells.

Preclinical models have also identified characteristics of lymphocytecultures that are required for successful ACT therapy. Until presently,the most important characteristic was thought to be the presence of highaffinity, tumor antigen specific CD8⁺ cells. It was also shown that CD4⁺cells were also required for effective treatment of some tumors [Surmanet al, J. Immunology 164, 562-565, 2000]. In addition, it has beendemonstrated that the presence of CD4⁺CD25⁺ T cells suppressautoimmunity and may be potent inhibitors of antitumor effects in mice[Shevach E. M. Nat. Rev. Immunol. 2, 389-400 (2002)]. This has led tothe conclusion that lymphodepleting subpopulations comprising thissignature may be beneficial for ACT therapy.

Some functional requirements of the cells for effective ACT wereelucidated in animal models. For example, the secretion of IFN-γ byinjected TILs was shown to significantly correlate with in vivoregression of murine tumors suggesting activation of T-cells by thetumor antigens (Barth, R. J., et al., (1991) J. Exp. Med. 173:647-658).Accordingly, selection of tumor-reactive T cells for adoptiveimmunotherapy may be effected by analyzing IFN-γ secretion followingexposure to tumor antigens. Despite its clinical importance, little isknown about the underlying composition and cellular interactions thatdetermine the degree of TIL reactivity as measured by IFN-γ secretionand consequentially on how to control this reactivity.

SUMMARY OF THE INVENTION

According to an aspect of some embodiments of the present inventionthere is provided a method of determining responsiveness to cancertreatment in a subject in need thereof, the method comprising analyzinga frequency of tumor infiltrating lymphocytes (TILs) having aCD8⁺CD28⁻CD152⁻ signature in a sample of the subject, wherein afrequency of TILs having the CD8⁺CD28⁻CD152⁻ signature above apredetermined level is indicative of a positive responsiveness to cancertreatment.

According to some embodiments of the invention, the method furthercomprises analyzing a frequency of TILs having a CD8⁺CD69⁺CD33⁻signature in the TIL sample, wherein a frequency of TILs having theCD8⁺CD69⁺CD33⁻ signature and the CD8⁺CD28⁻CD152⁻ signature above apredetermined level is indicative of a negative responsiveness to cancertreatment.

According to an aspect of some embodiments of the present inventionthere is provided a method of determining responsiveness to cancertreatment in a subject in need thereof, the method comprising analyzinga frequency of TILs having a CD8⁺CD28⁻CD152⁻ signature in a sample ofthe subject, wherein a frequency of TILs having a CD8⁺CD28⁻CD152⁻signature below a predetermined level is indicative of a negativeresponsiveness to cancer treatment.

According to some embodiments of the invention, the method furthercomprises analyzing a frequency of TILs having a CD94⁺ signature in thesample, wherein a frequency of TILs not having the CD8⁺CD28⁻CD152⁻signature whilst having a CD94+ signature above a predetermined level isfurther indicative of a negative responsiveness to cancer treatment.

According to an aspect of some embodiments of the present inventionthere is provided a method of predicting T cell responsiveness to acancer in a subject, comprising analyzing subpopulation markersignatures in a TIL sample of the subject, wherein a subpopulationmarker signature corresponding to a reactive marker signatures asdefined by FIG. 3A is indicative of T cell responsiveness and asubpopulation marker signature corresponding to a non-reactive markersignature as defined by FIG. 3A is indicative of a non T cellresponsiveness.

According to some embodiments of the invention, the cancer treatmentcomprises adoptive transfer therapy.

According to an aspect of some embodiments of the present inventionthere is provided a method of treating cancer in a subject in needthereof, the method comprising depleting lymphocytes from a sample ofTILs of the subject, wherein the lymphocytes express CD4, CD152 and CD28

According to some embodiments of the invention, the method furthercomprises depleting additional lymphocytes of the subject wherein theadditional lymphocytes express CD85 and/or CD94.

According to an aspect of some embodiments of the present inventionthere is provided a method of treating cancer in a subject in needthereof, the method comprising enriching for a subpopulation oflymphocytes from a sample of TILs of the subject, the subpopulationexpressing a CD8⁺CD28⁻CD152⁻ signature.

According to some embodiments of the invention, the method furthercomprises depleting an additional subpopulation of lymphocytes from thesample of CD8⁺CD28⁻CD152⁻ enriched TILs, the additional subpopulationexpressing a CD8⁺CD69⁺CD33⁻ signature.

According to some embodiments of the invention, the subject has a cancerselected from the group consisting of prostate cancer, renal cellcarcinoma, glioma and melanoma.

According to an aspect of some embodiments of the present inventionthere is provided a method of determining a reactivity of asubpopulation of TILs in a TIL sample, the method comprising:

(a) assaying an activity of a statistically significant number of TILsamples;

(b) analyzing the TIL samples by flow cytometry analysis of at leastthree markers per cell in order to classify subpopulations of cells,wherein at least one of the three markers is CD4 or CD8, at least asecond of the three markers is a cytokine or chemokine and at least athird of the three markers is an adhesion molecule, a co-inhibitoryreceptor, a co-stimulatory receptor or a protein set forth in Table 5;and

(c) analyzing a frequency of at least one subpopulation in the TILsample, wherein a frequency above a predetermined threshold indicatesthat the at least one subpopulation of cells is associated with theactivity.

According to some embodiments of the invention, the method furthercomprises removing the subpopulations following the analyzing thefrequency, wherein a subpopulation comprising a frequency lower than 1%is removed.

Unless otherwise defined, all technical and/or scientific terms usedherein have the same meaning as commonly understood by one of ordinaryskill in the art to which the invention pertains. Although methods andmaterials similar or equivalent to those described herein can be used inthe practice or testing of embodiments of the invention, exemplarymethods and/or materials are described below. In case of conflict, thepatent specification, including definitions, will control. In addition,the materials, methods, and examples are illustrative only and are notintended to be necessarily limiting.

BRIEF DESCRIPTION OF THE DRAWINGS

Some embodiments of the invention are herein described, by way ofexample only, with reference to the accompanying images. With specificreference now to the images in detail, it is stressed that theparticulars shown are by way of example and for purposes of illustrativediscussion of embodiments of the invention. In this regard, thedescription taken with the drawings makes apparent to those skilled inthe art how embodiments of the invention may be practiced.

In the drawings:

FIG. 1A is a schematic workflow of TIL characterization, analysis andreactivity control. As a first step TILs were extracted from surgicallyremoved tumor mass originating from metastatic melanoma patients. EachTIL was characterized by functional evaluation of IFN-γ secretion levelsfollowed by subpopulation fraction measurements using flow cytometry.This information was combined into a multi-parametric model forprediction and understanding of TIL reactivity. Following this analysis,the fractions of selected subpopulation were manipulated thus enablingcontrollability of TIL reactivity against melanoma.

FIG. 1B is a diagram of the central cell surface receptors definingspecific T-cell subpopulations with distinct functional states.

FIG. 2 are optimal cutoffs segregating reactive from nonreactive TILs asrepresented in a graphical form based on individual subpopulationfractions. For each subpopulation the blue and red dots indicate itsfraction in 39 reactive and 52 nonreactive TILs respectively.Subpopulation based linear classification (using a leave five outtesting scheme) produced MCCs in the range of 0 to 0.58. In general, MCCvalues range between −1 to +1 indicating completely wrong and perfectclassification respectively. An MCC=0 indicates random. The blackhorizontal bars indicate the optimal border between reactive andnonreactive TILs as determined by MCC.

FIGS. 3A-C are plots and diagrams illustrating that TIL reactivity islargely determined by its subpopulation fractions. (A) Reactive andnonreactive TILs exhibit distinct subpopulations signatures. Columns androws correspond to TILs and subpopulations respectively. The distancebetween TILs was calculated using Spearman correlation followed byhierarchical clustering. The rows were clustered similarly. The red andblue arrows represent nonreactive and reactive TILs respectively. Twomain clusters emerge characterized by CD4⁺ and CD8⁺ overabundantsubpopulations. These clusters also separate nonreactive from reactiveTILs (P<10⁻³). (B) A decision tree algorithm was used in order togenerate a simple set of rules for classifying TIL functionality. Theserules classify the TILs with 89% total accuracy. (C) Exact IFN-γ valuesof the reactive TILs can be described as a function of two subpopulationfractions with positive and negative weights:

IFN-γ(pg/ml)=63·(CD8⁺CD28⁻)−50·(CD8⁺CD69⁺CD33⁻)+253.

The IFN-γ levels can be described as a balance between two opposingsubpopulations with positive and negative effects and equal weights.

FIGS. 4A-C are graphs and diagrams showing that rational subpopulationmanipulation restores TIL anti-tumor reactivity accompanied by a shiftin subpopulation signature. (A) IFN-γ increases after TIL subpopulationmanipulation. IFN-γ levels of 12 TILs before and after inhibitorysubpopulation depletion are compared. 9 of the original nonreactive TILsshow significant increase in IFN-γ. Incubation of TILs in controlexperiments with culture media or unrelated melanoma indicates that theincrease in IFN-γ secretion does not occur spontaneously and isspecific. (B) Shift in reactivity can be explained in terms of a shiftin subpopulation signature prior and after depletion. The subpopulationfractions of 10 TILs prior and after subpopulation depletion weredetermined by flow cytometry. 8 of the original nonreactive TILs becamereactive, 7 of which also showed a shift from a nonreactivesubpopulation signature to a reactive one, indicated by the blue arrows.The two TILs that remained nonreactive exhibited either a minor changeor a negative change in subpopulation signature as indicated by the redarrows. (C) The transformation in reactivity of a TIL can be describedas a path in a two dimensional space. A simple representation of the TILreactivity signature was obtained by applying principal componentanalysis (PCA) [Ian T. Jolliffe, Principal component analysis (Springer,ed. second, 2002)], which is a method for dimensionality reduction atthe expense of loosing part of the data variance. The data was reducedfrom 35 to two dimensions. The x and y axes are principle componentscapturing 49% and 24% of the total variance in the data (FIGS. 1 lA-B).The figure shows a subspace that is overpopulated with reactive TILs.The change in reactivity can be visualized as a path from a nonreactiveTIL to a TIL that resides in the reactive subspace (for example seedotted arrow).

FIGS. 5A-C are tables of the final dataset of subpopulations after thefiltration procedure.

FIG. 6 is a flow chart of dataset filtering procedure.

FIG. 7 are graphs illustrating the distributions of subpopulationpercentages for single, double and triple staining of 91 TILs. Thex-axis is subpopulation percentage and the y-axis is the number of TILsthat had this percentage out of 91 TILs.

FIG. 8 is a map showing the correlation between pairs of receptors.Different groups of receptors are correlated and anti-correlated. Thepercentage of receptor occurrence in 91 TILs was measured using flowcytometry. Several groups of receptors show strong correlationsincluding A: (CD8⁺,CD69⁺,CD56⁺, Perforin,Grenzym); B:(CD85⁺,CD94⁺,CD152⁺,CD25⁺); and C: (CD4⁺,CD28⁺,CD33⁺). Furthermore, somegroups are anti-correlated, for example group A and C. The correlatedreceptors also share common functionality. For example group B containsthree co-inhibitory receptors. This suggests that receptors with commonfunctionality also share a common regulation at the population level.

FIG. 9 is a bar graph comparing the SVM prediction accuracy between thedatasets containing single, double and triple subpopulations as well asthe filtered dataset.

FIG. 10 is a Venn diagram illustrating that reactive TILs share asimilar subpopulation signature. A simple representation of the TILreactivity signature was obtained by applying principal componentanalysis (PCA). This technique enables a reduction in dimensionality atthe expense of loosing some of the data variance. FIG. 10 shows amapping from the original 33 dimensional data into two dimensions.K-means unsupervised clustering generated two distinct clusters thatwere enriched for reactive and nonreactive TILs (Fischer exact P<10⁻³).The x and y axes explain 60% and 11% of the variance in the datarespectively. Another property of TIL reactivity emerges from thesubspaces in which each functional state resides. While the reactiveTILs occupy a defined subspace of subpopulation combinations, asindicated by the high density of blue dots, the nonreactive TILs,indicated by red dots, are dispersed

FIGS. 11A-B are bar graphs illustrating the subpopulation coefficientsfor the first and second principle components shown in FIG. 4C.

DESCRIPTION OF SPECIFIC EMBODIMENTS OF THE INVENTION

The present invention, in some embodiments thereof, relates to T cellpopulations capable of treating cancer.

Adoptive cell transfer (ACT) therapy for patients with cancer relies onthe ex vivo generation of highly active tumor, specific lymphocytes, andtheir administration in large numbers to the autologous host.

Preclinical models have identified characteristics of lymphocytecultures that are required for successful ACT therapy. Until presently,the most important characteristic was thought to be the presence of highaffinity, tumor antigen specific CD8⁺ cells. It was also shown that CD4⁺cells were also required for effective treatment of some tumors [Surmanet al, J. Immunology 164, 562-565, 2000]. In contrast, it has beendemonstrated that the presence of CD4⁺CD25⁺ T cells suppressautoimmunity and may be potent inhibitors of antitumor effects in mice[Shevach E. M. Nat. Rev. Immunol. 2, 389-400 (2002)]. This has led tothe conclusion that lymphodepleting subpopulations comprising thissignature may be beneficial for ACT therapy.

The present inventors have devised a novel method for studying theunderlying composition and cellular interactions that determine thedegree of TIL reactivity. This method, summarized in FIG. 1A, is basedon measuring frequencies of subpopulation fractions and constructing a“subpopulations signature” for each TIL.

Using a decision tree algorithm, three subpopulations were identified asbeing important predictors of reactivity (FIG. 3B). These subpopulationsinclude CD8⁺CD28⁻CD152⁻, CD94 ⁺ and CD8⁺CD69⁺CD33⁻.

Knowledge of subpopulations that predict the reactivity of the TILsample allowed the present inventors to control reactivity thereof.Accordingly, exploitation of this knowledge has lead to the generationof TIL populations of enhanced reactivity.

Whilst further reducing the present invention to practice, the presentinventors have shown that it is possible to deplete non-reactive TILs oflymphocytes of particular subpopulation signatures and restore TILanti-tumor reactivity (FIGS. 4A-C).

Thus, according to one aspect of the present invention, there isprovided a method of determining a reactivity of a subpopulation of TILsin a TIL sample, the method comprising:

(a) assaying an activity of a statistically significant number of TILsamples;

(b) analyzing the TIL samples by flow cytometry analysis of at leastthree markers per cell in order to classify subpopulations of cells,wherein at least one of the three markers is CD4 or CD8, at least asecond of the three markers is a cytokine or chemokine and at least athird of the three markers is an adhesion molecule, a co-inhibitoryreceptor, a co-stimulatory receptor or a protein set forth in Table 5;and

(c) analyzing a frequency of at least one subpopulation in the TILsample, wherein a frequency above a predetermined threshold indicatesthat the at least one subpopulation of cells is associated with theactivity.

As used herein, the term “reactivity” refers to an ability of the TILsto at least inhibit cancer progression and optimally promote regressionof same (either partially, or completely).

As used herein, the term “tumor-infiltrating lymphocytes” refers towhite blood cells of a subject afflicted with a cancer (such asmelanoma), that have left the blood stream and have migrated into atumor. Thus, tumor-infiltrating lymphocytes may have tumor specificity.

Such lymphocytes can be isolated from an individual (e.g. during a tumorbiopsy) to be treated by methods known in the art and cultured in vitro(Kawakami, Y. et al. (1989) J. Immunol. 142: 2453-3461). Lymphocytes maybe cultured in media such as RPMI or RPMI 1640 or AIM V for 1-10 weeks.An exemplary method for obtaining TILs includes plating viable cells(e.g. 1×10⁶) of a single-cell suspension of enzymatically digestedexplant of metastatic melanoma. It will be appreciated that the TILs maybe isolated from fresh tumors or from frozen tissue (at the cost oflower yield).

The TIL samples of the present invention may be obtained from anymammalian species, such as human.

As mentioned, the method of this aspect of the present invention iseffected by assaying the activities of a statistically significantnumber of TIL samples. It will be appreciated that the number ofstatistically significant TILs is dependent on the number of markersthat are analyzed per cell.

Thus, typically when three marker per cell are analyzed, thestatistically significant number of samples is greater than about 10.According to another embodiment, the statistically significant number ofsamples is greater than about 50. According to another embodiment, thestatistically significant number of samples is greater than about 75.According to another embodiment, the statistically significant number ofsamples is greater than about 100. According to another embodiment, thestatistically significant number of samples is greater than about 150.According to another embodiment, the statistically significant number ofsamples is greater than about 200.

Exemplary methods of assaying activities of TIL samples include ⁵¹CRrelease cytotoxicity assays (Cerundolo, V. et al. (1990) Nature345:449-452) or lymphokine assays such as IFN-γ or TNF secretion assays[Schwartzentruber, D. et al., (1991) J. of Immunology 146:3674-3681].

As mentioned herein above, the method of this aspect of the presentinvention is further effected by analyzing the TIL samples by flowcytometry analysis of at least three markers per cell in order toclassify subpopulations of cells.

As used herein, the term “flow cytometry” refers to an assay in whichthe proportion of a material (e.g. lymphocyte comprising a particularmaker) in a sample is determined by labeling the material (e.g., bybinding a labeled antibody to the material), causing a fluid streamcontaining the material to pass through a beam of light, separating thelight emitted from the sample into constituent wavelengths by a seriesof filters and mirrors, and detecting the light.

A multitude of flow cytometers are commercially available including fore.g. Becton Dickinson FACScan and FACScaliber (BD Biosciences, MountainView, Calif.). Antibodies that may be used for FACS analysis are taughtin Schlossman S, Boumell L, et al, [Leucocyte Typing V. New York: OxfordUniversity Press; 1995] and are widely commercially available.

According to one embodiment, the markers are cell surface antigens.

According to this aspect of the present invention at least one of thethree markers is a cytokine or chemokine

Exemplary cytokine and chemokine makers contemplated by the presentinvention, include, but are not limited to those set forth in Table 1.

TABLE 1 Antigen Other Name Names Structure Function CD117 c-kit, SCFRIgSF, RTK SCF receptor, hematopoietic progenitor familydevelopment/differentiation CDw119 IFNγR IFN-γ Rα, w/ IFN-γ AF-1, hostdefense CD120a TNFR-I TNFRSF receptor for both TNF-α and TNF-β CD120bTNFR-II TNFRSF receptor for both TNF-α and TNF-β CD121a IL-1R type IIgSF binds IL-1α and IL-1β, IL-1 signaling CDw121b IL-1R, type IgSFbinds IL-1α and IL-1β, negative signals II CD122 IL-2Rβ CRSF IL-2Rβ andIL-15Rβ, signal transduction CDw123 IL-3R CRSF IL-3Rα, w/ CDw131 CD124IL-4R CRSF IL-4Rα, w/ CD132 or IL-13Rα, T cell growth/differentiationCDw125 IL-5R CRSF IL-5Rα, w/ CDw131 CD126 IL-6R IgSF, IL-6Rα, w/ CD130CRSF CD127 IL-7R CRSF IL-7Rα, w/ CD132, B and T cell development CD130IL-6Rβ, CRSF IL-6Rβ, IL-6, IL-11, LIF, CNF signals gp130 CDw131 IL-3RCRSF w/ α subunits of IL-3R, IL-5R, and GM-CSFR, common β signaltransduction CD132 Common γ CRSF subunit of IL-2R, IL-4R, IL-7R, IL-9R,and IL-15R, signal transduction CD181 CXCR1, GPCR1 binding of IL-8induces chemotaxis of neutrophils IL-8RA family CD182 CXCR2, GPCR1binding of IL-8 induces chemotaxis of neutrophils IL-8RB family CD183CXCR3 TM7SF IP-10, Mig and I-TAC receptor, T cell recruitment toinflammatory sites, enhancement of Th1 response CD184 CXCR4, TM7SF SDF-1receptor, X4 HIV-1 coreceptor fusin CD185 CXCR5, GPCR1 w/ chemokine BLC,possible regulatory function in BLR1 family Burkitt Lymphomagenesisand/or B differentiation, activation of mature B CDw186 CXCR6, GPCR1receptor for CXCL16 and coreceptor for SIV, strains BONZO family ofHIV-2 and m-tropic HIV-1 CD191 CCR1, GPCR1 binds C-C type chemokines andtransduces signal by MIP-1αR, family increasing intracellular calciumion levels RANTES-R CD192 CCR2, GPCR1 binds MCP-1, MCP-3 & MCP-4,alternative MCP-1-R family coreceptor with CD4 for HIV-1 infection CD193CCR3, GPCR1 binds eotaxin, eotaxin-3, MCP-3, MCP-4, RANTES CKR3 family &MIP-1δ, alternative coreceptor w/ CD4 for HIV-1 infectiongg CD195 CCR5MIP-1α, MIP-1β and RANTES receptor, R5 HIV-1 coreceptor CD196 CCR6,GPCR1 binds MIP-3α/LARC LARC family receptor, DRY6 CD197 CCR7 6Ckine andMIP-2β receptor CDw198 CCR8, GPCR1 allergic inflammation, alternativecoreceptor with GPRCY6, family CD4 for HIV-1 infection TER1 CDw199 CCR9,GPCR1 binds SCYA25/TECK, alternative coreceptor with GPR-9-6 family CD4for HIV-1 infection CDw210 IL-10-R IL-10 receptor, signal transductionCD212 IL-12-R β1 binds IL-12 w/ high affinity, associates w/ IL-12receptor β2 CD213a1 IL-13-R α1 binds IL-13 w/ low affinity, w/ CD124CD213a2 IL-13-R α2 binds IL-13 w/ high affinity CDw217 IL-17-R IL-17receptor CDw218a IL-18Rα, IL-1R binds IL-18, activation of NF-κB IL-1Rrpfamily CDw218b IL-18Rβ, IL-1R heterodimeric receptor with IL-18Rα toenhance IL- IL18RAP family 18 binding CD234 Duffy, Duffy antigenchemokine receptor DARC CD25 Tac, p55 type I TM IL-2Rα, w/ IL-2Rβ and γto form high affinity complex CD30 Ki-1 TNFRSF CD153 receptor, lymphproliferation/apoptosis CD46 MCP CCRSF membrane cofactor protein, bindsC3b & C4b allowing degradation by Factor I, measles virus receptor CD105Endoglin homodimer cellular response to TGF-β1 CD110 MPL, TPO-R CRSFthrombopoietin receptor, megakaryocyte progenitor cellgrowth/differentiation CD114 G-CSFR CRSF myeloiddifferentiation/proliferation CD115 M-CSFR, IgSF, RTK CSF-1R, monocyticdifferentiation/proliferation c-fms family CD116 GM-CSFRα CRSF w/ commonβ, myeloid differentiation/proliferation CD135 Flt3/Flk2 RTK tyrosinekinase, binds FLT3 ligand, early lymph family development CDw136 MSP-R,RTK migration, morphological change and proliferation of RON familydifferent target cells CD140a PDGFRα RTK binds PDGF A and B familyCD140b PDGFRβ RTK binds PDGF B family CD254 TRANCE, TNFSF binds OPG andRANK, osteoclast differentiation, RANKL, enhances DC to stimulatenaïve-T proliferation OPGL CD256 APRIL, TNFSF binds TACI & BCMA, Bproliferation TALL-2 CD257 BLyS, TNFSF B cell growth factor &costimulator of Ig production BAFF, TALL-1 CD258 LIGHT, TNFSF bindsLTBR, T proliferation, receptor for HVEM HVEM-L CD261 TRAIL-R1, TNFRSFcontains death domain, apoptosis via FADD & DR4 caspase-8 CD262TRAIL-R2, TNFRSF contains death domain, apoptosis via FADD and DR5caspase-8 CD263 TRAIL-R3, TNFRSF receptor for TRAIL but lacks deathdomain DcR1, LIT CD264 TRAIL-R4, TNFRSF binds TRAIL but containstruncated death domain TRUNDD, DcR2 CD265 RANK, TNFRSF binds TRANCE,osteoclastogenesis, T-DC TRANCE- interactions R, ODFR CD266 TWEAK-R,TNFRSF TWEAK receptor, cell-matrix interactions and FGF- endoth growthand migration inducible 14 CD326 Ep-CAM, TM tyr growth factor receptor?Ly74 kinase CD331 FGFR1, binds FGF, high affinity receptor forfibroblast Fms-like growth factors tyrosine kinase-2, KAL2, N- SAM CD332FGFR2, TM RTK binds FGF, high affinity receptor for fibroblast BEK,family growth factors KGFR CD333 FGFR3, TM RTK binds FGF, high affinityreceptor for fibroblast ACH, family growth factors CEK2 CD334 FGFR4, TMRTK binds FGF, high affinity receptor for fibroblast JTK2, TKF familygrowth factors AITRL TNFSF18, TL6, GITRL CMKLR1 chemokine- GPCR bindschemerin, pDC recruitment, bone development like 7TM, receptor 1chemokine receptor DcR3 TR6, Soluble Fas decoy receptor, tumor evasionTNFRSF6B HVEM TNFRSF14, TNFRSF receptor for LIGHT, LT-α, BTLA, HerpesSimplex TR2 Virus, lymphocyte activation IL-15Rα binds to IL-15, w/IL-2RB and common γ, IL-15 trans-presentation TLR5 TIL3 TLR familyinteracts w/ microbial lipoproteins, NF-κB, responds to Salmonella TLR6TLR family interacts w/ microbial lipoproteins, protein sequence similarto hTLR1; regulates TLR2 response TLR7 TLR family TLR8 TLR family TLR10TLR family most closely related to TLR1 and TLR6 TSLPR heterodimer bindsTSLP (Thymic Stromal Lymphopoietin) to with IL- activate DC 7Rα/CD127

According to this aspect of the present invention at least one of thethree markers is an adhesion molecule, a co-inhibitory receptor, aco-stimulatory receptor or a relevant protein such as those set forth inTable 5.

Exemplary adhesion molecules contemplated by the present invention areset forth in Table 2.

TABLE 2 Antigen Name Other Names Structure Function CD11a LFA-1,integrin Integrin CD11a/CD18 receptor for ICAM-1, -2, -3, αL familyintercellular adhesion, T costimulation CD50 ICAM-3 IgSF adhesion,costimulation CD73 GPI-linked ecto-5′-nucleotidase, nucleoside uptake, Tcostimulation, lymph adhesion CD99 MIC2, E2 T cell activation, adhesionCD106 VCAM-1 IgSF VLA-4(CD49d/CD29) receptor, leukocyte adhesion,migration, costimulation CD2 T11, LFA-2, IgSF CD58 ligand, adhesion, Tcell activation SRBC-R CD9 p24, MRP-1 TM4SF cellular adhesion andmigration CD15 Lewis-x, Lex CHO adhesion CD15s Sialyl Lewis X CHO CD62Land CD62P ligand, adhesion CD15u Sulfated Lewis X CHO adhesion CD18Integrin β2 Integrin w/ CD11a, b & c, adhesion family CD22 BL-CAM,Siglec-2 IgSF, adhesion, B-mono, B-T interactions sialoadhesins CD31PECAM-1 IgSF CD38 receptor, adhesion CD33 p67, Siglec-3 IgSF, adhesionsialoadhesins CD34 Sialomucin, stem cell marker, adhesion, CD62Lreceptor type I TM CD35 CR1 CCRSF complement receptor 1, binds C3b andC4b, adhesion, phagocytosis CD36 GPIV ECM receptor, adhesion,phagocytosis CD42a GPIX LRRF complex w/ CD42b, c and d, receptor for vWFand thrombin, platelet adhesion to subendothelial matrices CD42b GPIbaLRRF complex w/ CD42a, c and d, binds to vWF and thrombin, plateletadhesion/activation CD43 Leukosialin, Sialomucin, inhibition of T cellinteraction, CD54R, sialophorin type I TM adhesion CD44 H-CAM, Pgp-1hyaladherin binds hyaluronic acid, adhesion family CD44R CD44v adhesion,metastasis CD47 IAP IgSF leukocyte adhesion, migration, activation CD48Blast-1 IgSF cell adhesion CD49a VLA-1 Integrin integrin α1, adhesion,CD49a/CD29 binds family collagen and laminin CD49b VLA-2 Integrinintegrin α2, adhesion, CD49b/CD29 binds family collagen and lamininCD49c VLA-3 Integrin integrin α3, adhesion, CD49c/CD29 binds familylaminin, fibronectin and collagen CD49d VLA-4 Integrin integrin α4,adhesion, CD49d/CD29 binds family fibronectin, VCAM-1 & MAdCAM-1 CD49eVLA-5 Integrin integrin α5, adhesion, CD49e/CD29 binds familyfibronectin CD49f VLA-6 Integrin integrin α6, adhesion, CD49f/CD29 bindsfamily laminin CD51 Vitronectin Integrin integrin αv, adhesion,CD51/CD61 binds receptor family vitronectin, vWF, fibrinogen andthrombospondin CD56 NCAM IgSF adhesion CD58 LFA-3 IgSF CD2 receptor,adhesion CD61 GPIIIa Integrin integrin β3, adhesion, CD41/CD61 or familyCD51/CD61 mediate adhesion to ECM CD62P P-selectin, Selectin CD162,CD15s receptor, adhesion, neutrophil PADGEM family rolling,platelet-neutrophil and platelet-mono interactions CD66a BGP-1, NCA-160IgSF, CEA cell adhesion family CD66b CD67, CGM6 IgSF, CEA cell adhesion,neutrophil activation family CD66c NCA IgSF, CEA cell adhesion familyCD66e CEA IgSF, CEA cell adhesion family CD96 TACTILE IgSF adhesion ofactivated T and NK CD100 cell adhesion, cellular activation CD104 β4integrin Integrin w/ integrin α6 (CD49f), cell adhesion, familydifferentiation, metastasis CD112 PRR2, Nectin-2 IgSF intercellularadhesion CDw113 PVRL3, Nectin3 IgSF adhesion molecule that interactswith afadin CD138 Syndecan-1 Syndecan receptor for ECM, cell morphologyfamily CD144 VE-Cadherin, Cadherin adhesion, cell-cell interactionCadherin-5 family CD146 MUC18, S-endo IgSF adhesion CD147 Neurothelin,IgSF adhesion basoglin CD151 PETA-3 cell adhesion CD162 PSGL-1 Mucinfamily CD62P, CD62L ligand, adhesion, rolling CD166 ALCAM IgSF CD6ligand, adhesion CD167a DDR1 RTK family tyrosine kinase, adhesion tocollagen CD168 RHAMM adhesion, tumor migration, metastasis CD169sialoadhesin, IgSF, adhesion, cell-cell and cell-matrix Siglec-1sialoadhesins interactions, binds CD227 on breast cancer cells and CD43on T cells CD170 Siglec-5, CD33- IgSF, adhesion like2 sialoadhesinsCD172a SIRPγ adhesion, complex w/ CD47 CD222 IGF-II R, Type I TMadhesion, tumor growth, a receptor for TGFβ- mannose-6 LAP, plasminogen,proliferin, truncated form phosphate-R (220 kD) found in serum CD227MUC1, EMA Mucin adhesion, signaling, binds CD169, CD54, & family, type Iselectins TM CD229 Ly-9 IgSF adhesion CD242 ICAM-4 IgSF adhesion,Landsteiner-Wiener blood group CD309 VEGFR2, KDR Type III TM binds VEGF,regulates adhesion and cell tyr kinase signaling CD312 EMR2 EGFR-7TMcell adhesion and migration for phagocytosis ASV CD318 CDCP1, Type I,ASV cell adhesion with ECM SIMA135 CD322 JAM2, VE-JAM IgSF celladhesion, lymphocyte homing to secondary lymphoid organs CD324E-Cadherin, cadherin SF cell adhesion, homotypic interaction & bindsUvomorulin αE/β7 CDw325 N-Cadherin, cadherin SF cell adhesion, neuronalrecognition NCAD CDw327 SIGLEC6 IgSF adhesion, membrane-bound & secretedforms CDw328 SIGLEC7, IgSF sialic-acid dependent adhesion, inhibit NKAIRM-1 activation, hemopoiesis CDw329 SIGLEC9 IgSF sialic-acid dependentadhesion molecule CD11b Mac-1, integrin Integrin binds CD54, ECM, iC3bαM family CD11c p150, 95, CR4, Integrin binds CD54, fibrinogen and iC3bintegrin αX family CD24 BA-1 GPI-linked binds P-selectin CD29 Integrinβ1 Integrin w/ CD49a (VLA-1) receptor for VCAM-1, family MAdCAM-1 andECM CD41 gpIIb Integrin w/ CD61 forms GPIIb, binds fibrinogen, familyfibronectin, vWF, thrombospondin, platelet activation and aggregationCD42c GPIbb LRRF complex w/ CD42a, b, d CD42d GPV LRRF complex w/CD42a-c CD54 ICAM-1 IgSF receptor for CD11a/CD18 (LFA-1), CD11b/CD18(Mac-1) and rhinovirus CD62E E-selectin, Selectin binds CD15s, cellrolling, metastasis ELAM-1 family CD62L L-selectin, Selectin CD34,GlyCAM, and MAdCAM-1 receptor, LECAM-1 family leukocyte homing,tethering, rolling CD66d CGM1 IgSF, CEA family CD66f PSG, Sp-1 IgSF, CEAimmune regulation, protects fetus from family maternal immune systemCD69 AIM C-type lectin signal transduction CD75 CHO lactosaminesSialoglycan family CD75s CHO α-2,6-sialylated lactosamines (previouslySialoglycan CDw75 and CDw76) family CD103 HML-1, α6, Integrin w/integrin β7, binds E-cadherin, lymph integrin αE family homing/retentionCD111 PRR1, Nectin-1 IgSF CD133 AC133, prominin- TM5SF like 1 CD141Thrombomodulin C-type lectin initiation of protein C anticoagulantsignal CD156a ADAM8 leukocyte extravasation CD280 ENDO180, C-type lectinmannose receptor, collagen matrix UPARAP SF remodeling and endocyticrecycling CD303 BDCA2, HECL C-type lectin inhibit IFN-α production SFASV CD321 JAM1, F11 IgSF, Type I, tight junctions receptor ASV Integrinw/ αv subunit, vitronectin receptor β5

Exemplary co-stimulatory receptors contemplated by the present inventionare set forth in Table 3.

TABLE 3 Antigen Other Name Names Structure Function CD6 T12 ScavengerCD166 receptor, T cell differentiation/costimulation R SF CD7 IgSF Tcostimulation CD26 DPP IV type II dipeptidyl peptidase, T costimulation,HIV entry TM CD27 T14 TNFRSF CD70 receptor, T costimulation CD28 Tp44,T44 IgSF CD80, CD86 receptor, T costimulation CD40 TNFRSF CD154receptor, B differentiation/costimulation, isotype-switching, rescues Bcells from apoptosis CD60a GD3 CHO costimulation CD70 Ki-24 TNFSF CD27ligand, T and B cell costimulation CD80 B7, B7-1, IgSF binds to CD28,CD152, T costimulation BB1 CD81 TAPA-1 TM4SF complex w/ CD19 & CD21,signaling, T costimulation CD86 B70, B7-2 IgSF binds to CD28, CD152, Tcostimulation CD102 ICAM-2 IgSF binds CD11a/CD18, costimulation CDw1374-1BB TNFRSF T costimulation CD150 SLAM IgSF costimulation,proliferation, Ig production, measles virus receptor CD152 CTLA-4 IgSFCD80 and CD86 receptor, negative regulation of T cell costimulationCD153 CD30L TNFSF CD30 ligand, T costimulation CD154 CD40L, TNFSF CD40ligand, B and DC costimulation gp39, TRAP CD160 BY55 IgSF costimulationCD171 L1 IgSF kidney morphogenesis, lymph node architecture, Tcostimulation, neurohistogenesis, homotypic interaction, binds CD9,CD24, CD56, CD142, CD166, integrins CD252 OX- TNFSF T costimulation40Ligand, gp34 CD273 B7DC, PD- IgSF PD-1 receptor, costimulation orsuppression of T L2, proliferation PDCD1L2 CD274 B7-H1, PD- IgSF PD-1receptor, costimulation of lymphocytes L1 CD275 B7-H2, B7 Familycostimulation, cytokine production ICOSL, B7- RP1, GL50 CD276 B7-H3 B7costimulation, T activation Family, ASV CD278 ICOS, CD28 binds ICOS-L, Tcostimulation AILIM family CD314 NKG2D, Type II binds MHC class I, MICA,MICB, Rae1 & ULBP4, KLR lectin-like activates cytolysis and cytokineproduction, receptor costimulation CD38 T10 ecto-ADP-ribosyl cyclase,cell activation CD45 LCA, T200, tyrosine phosphatase, enhanced TCR & BCRsignals B220 CD45RA exon A isoforms of CD45 CD45RB exon B isoforms ofCD45 CD45RO isoform of CD45 lacking A, B, C exons CD63 LIMP, TM4SFlysosomal membrane protein, moves to cell surface LAMP-3 afteractivation CD83 HB15 IgSF CD101 V7, p126 IgSF T cell activation CD134OX-40 TNFRSF T cell activation, differentiation, apoptosis CD148HPTP-eta tyrosine phosphatase R Type III CD161 NKR-P1A C-type NKcell-mediated cytotoxicity lectin CD221 IGF-1 R binds IGF w/ highaffinity, signaling, cell proliferation/differentiation CD243 MDR-1, ionpump p170, P-gp CD244 2B4 type II NK activation, CD48 ligand TM CD247TCRz RTK TCR complex subunit, coupling of antigen recognition family tosignaling CD277 BT3.1, B7/BT T activation butyrophilin family, SF3 A1,ASV BTF5 CD319 CRACC, Ig TM regulate T and NK cells SLAMF7 CD335 NKp46,Ly- IgSF activates NK cells upon non-MHC ligand binding 94 homolog CD336NKp44, Ly- IgSF activates NK cells upon non-MHC ligand binding 95homolog CD337 NKp30, IgSF activates NK cells upon non-MHC ligand bindingLy117 4-1BB CD137L TNFSF T costimulation Ligand AITR TNFRSF18,costimulation GITR SLP-76 T cell receptor mediated signaling T-bettranscription factor, T development/differentiation TCR αβ antigenrecognition TCR γδ antigen recognition

Exemplary co-inhibitory receptor markers contemplated by the presentinvention are set forth in Table 4.

TABLE 4 Antigen Other Name Names Structure Function CD158a p58.1 IgSF,KIR inhibition of NK cell cytolytic activity, MHC class-I familyspecific NK receptor CD158b p58.2 IgSF, KIR inhibition of NK cellcytolytic activity, MHC class-I family specific NK receptor CD85 IgSF,inhibition of NK, T cell cytolytic function ILT/LIR family CD200 OX-2inhibition of immune response CD272 BTLA IgSF HVEM receptor, inhibitoryresponse CD294 CRTH2. GPCR- binds prostaglandin D2, stimulatory effectson Th2, GPR44 7TM allergic inflammation CD305 LAIR1 IgSF, ASV inhibitoryreceptor on NK and T cells CD77 Gb3, Pk apoptosis blood group CD94 KP43C-type complex w/ NKG2, inhibits NK function lectin CD118 LIFR, gp190Type I membrane-bound involved in signal transduction, CRSF & solubleform inhibits activity of LIF secreted forms CD159c NKG2C Type II C- w/MHC class I HLA-E molecules, forms Type heterodimer with CD94 LectinCD253 TRAIL, TNFSF death Apo-2L, TL2, TNFSF10 CD279 PD1, SLEB2 B7-H1 &B7-DC receptor, autoimmune disease and peripheral tolerance CD300cCMRF35A, IgSF unknown LIR B7-H4 B7-S1, B7x B7 family may interact withBTLA (?), inhibition BAMBI TGFBR TGFBR pseudoreceptor for TGF-β (shortcytoplasmic domain), growth inhibition DR6 TR7 TNFRSF death, Th2response Foxp3 SCURFIN Fox family transcription factor, upregulated in Tregs forkhead TWEAK TNFSF12, TNFSF death APO3L

Other markers contemplated by the present invention include those setforth in Table 5 herein below.

TABLE 5 Antigen Name Other Names Structure Function CD88 C5aR TM7SF C5areceptor, granulocyte activation CD89 FcαR IgSF IgA receptor,phagocytosis, degranulation, respiratory burst CD5 T1, Tp67 ScavengerCD72 receptor, TCR or BCR signaling, T-B R SF interaction CD159a NKG2Aw/ CD94, NK cell receptor CD163 130 kD Scavenger receptor SF CD173 Bloodgroup CHO H type 2 CD174 Lewis Y CHO CD175 Tn CHO CD175s Sialyl-Tn CHOCD176 Thomson CHO Friedrenreich Ag CD177 NB1 CD178 FasL, CD95L TNFSFCD95 ligand, apoptosis, immune privilege, soluble form in serum CD2RT11-3 IgSF activation-dependent form of CD2 CD3γ, CD3δ T3 IgSF w/ TCR,TCR surface expression/signal transduction CD3ε T3 IgSF w/ TCR, TCRsurface expression/signal transduction CD4 T4 IgSF MHC class IIcoreceptor, HIV receptor, T cell differentiation/activation CD8a T8,Leu-2 IgSF MHC class I coreceptor, receptor for some mutated HIV-1, Tcell differentiation/activation CD8b IgSF CD14 LPS-R GPI-linked receptorfor LPS/LBP, LPS recognition CD16a FcγRIIIA IgSF component of lowaffinity Fc receptor, phagocytosis and ADCC CD16b FcγRIIIB IgSFcomponent of low affinity Fc receptor, phagocytosis and ADCC CD23 FcεRIIC-type CD19-CD21-CD81 receptor, IgE low affinity lectin receptor, signaltransduction CD32 FcγRII IgSF low affinity Fc receptor for aggregated Igand immune complexes CD39 NK, mac, Langerhans cells, DC, Bact CD55 DAFGPI-linked binds C3b, complement regulation CD57 HNK-1, Leu-7 CD64 FcγRIIgSF high affinity receptor for IgG, phagocytosis and ADCC CD71 T9transferrin receptor, iron uptake CD74 Ii, invariant MHC class IItraffic and function chain CD87 UPA-R GPI-linked urokinase plasminogenactivator receptor, inflammatory cell invasion, metastasis CD91 LDLRreceptor for α-2-macroglobulin family CD95 Apo-1, Fas TNFRSF FasL(CD178) receptor, apoptosis CD107a LAMP-1 a lysosomal membrane proteinCD107b LAMP-2 a lysosomal membrane protein CD156b TACE/ADAM cleavesmembrane proteins (TNF, TGFα) to 17 generate soluble forms CDw156cADAM10 Peptidase proteolytic cleavage of cell-surface molecules M12Bincluding Notch, TNF-α, APP and ephrin-A2 family CD165 AD2, gp37 lymphsubset, mono, immature thymocytes, platelets CD281 TLR1 TLR familyinnate immunity, w/ TLR2 CD282 TLR2 TLR family binds dsRNA, response tobacterial lipoproteins, innate immunity CD283 TLR3 TLR binds dsRNA,innate immunity family, ASV CD284 TLR4 TLR binds LPS, innate immunityfamily, ASV CD289 TLR9 TLR family binds CpG-DNA, innate immunity CDw338ABCG2, GPCR 7TM multi-drug resistance transporter BCRP, Bcrp1, MXRFcεRIα high-affinity tetramer triggers IgE-mediated allergic reactionsIgE receptor complex Granzyme B Granzyme-2, Peptidase target cellapoptotic lysis, cell-mediated CTLA-1 S1 family immune responses HLA-ABCcell-mediated immune response & tumor surveillance HLA-DR presentationof peptides to CD4+ T lymphocytes MICA/MICB MHC Class unregulated onepith after shock, NKG2D I-related receptors proteins p38 SAP/MAP rolein cytolytic activity kinase Perforin cytolytic protein Stro-1 surfacemarker for immature mesenchymal cells

Following flow cytometry analysis, each TIL sample can be classifiedinto subpopulations as described in the Examples section below. Bymeasuring the frequency of each subpopulation in an already definedreactive/non-reactive sample, the significance of the subpopulation maybe effected. Thus subpopulations above a predetermined threshold in areactive TIL sample may be classified as positive predictors.Conversely, subpopulations above a predetermined threshold in anon-reactive TIL sample may be classified as negative predictors.

The predetermined thresholds may be determined using mathematicalalgorithms as exemplified in the FIG. 3B of the Examples section below.

According to an embodiment of this aspect of the present invention, onlysubpopulations above a frequency of about 1% are considered significant.

As explained in the Examples section below, the present inventorsutilized this method to screen a significantly relevant number of TILs(91) and incorporated all the information gleaned into a diagrammaticrepresentation of reactive marker signatures (FIG. 3A). Such markersignatures may be used to predict T cell responsiveness to a cancer in asubject.

Thus, according to another aspect of the present invention, there isprovided a method of predicting T cell responsiveness to a cancer in asubject, comprising analyzing subpopulation marker signatures in a TILsample of the subject, wherein a subpopulation marker signaturecorresponding to a reactive marker signatures as defined by FIG. 3A isindicative of T cell responsiveness and a subpopulation marker signaturecorresponding to a non-reactive marker signature as defined by FIG. 3Ais indicative of a non T cell responsiveness.

As used herein, the term “signature” refers to an expression pattern ofthe indicated markers.

According to this aspect of the present invention the cancer to which Tcell responsiveness is predicted includes melanoma, lung carcinoma,breast cancer, colon cancer, prostate cancer, ovarian carcinoma, renalcell carcinoma, glioma and the like. The cancer may be metastatic ornon-metastatic.

As used herein, the term “melanoma” refers to metastatic melanomas,melanomas derived from either melanocytes or melanocytes related nevuscells, melanocarcinomas, melanoepitheliomas, melanosarcomas, melanoma insitu, superficial spreading melanoma, nodular melanoma, lentigo malignamelanoma, acral lentiginous melanoma, invasive melanoma or familialatypical mole and melanoma (FAM-M) syndrome. Such melanomas in mammalsmay be caused by, chromosomal abnormalities, degenerative growth anddevelopmental disorders, mitogenic agents, ultraviolet radiation (UV),viral infections, inappropriate tissue expression of a gene, alterationsin expression of a gene, or carcinogenic agents.

By determining reactivity of subpopulations of TILs in TIL samples, thepresent inventors identified three subpopulations as being importantpredictors of reactivity. These subpopulations include CD8⁺CD28⁻CD152⁻,CD94 ⁺ and CD8⁺CD69⁺CD33⁻.

Thus according to yet another aspect of the present invention, there isprovided a method of determining responsiveness to cancer treatment in asubject in need thereof, the method comprising analyzing a frequency oftumor infiltrating lymphocytes (TILs) having a CD8⁺CD28⁻CD152⁻ signaturein a sample of the subject, wherein a frequency of TILs having theCD8⁺CD28⁻CD152⁻ signature above a predetermined level is indicative of apositive responsiveness to cancer treatment.

According to this aspect of the present invention, the cancer treatmentis any treatment which involves the use of TILs, such as for exampleadoptive transfer therapy.

According to this aspect of the present invention, the number of TILs ina TIL sample having a CD8⁺CD28⁻CD152⁻ signature is greater than 25%,more preferably greater than 35% and even more preferably greater than45%.

The present inventors have shown that a TIL sample comprising asignificant percentage of CD8⁺CD69⁺CD33⁻ bearing lymphocytes whichalready comprises a significant percentage of CD8⁺CD28⁻CD152⁻ bearinglymphocytes is indicative of a negative responsiveness to cancer.

According to this embodiment, the predetermined level of CD8⁺CD69⁺CD33⁻bearing lymphocytes is typically greater than about 40%, more preferablygreater than about 50% and even more preferably greater than about 60%.

According to still another aspect of the present invention, there isprovided a method of determining responsiveness to cancer treatment in asubject in need thereof, the method comprising analyzing a frequency oftumor infiltrating lymphocytes (TILs) having a CD8⁺CD28⁻CD152⁻ signaturein a sample of the subject, wherein a frequency of TILs having theCD8⁺CD28⁻CD152⁻ signature below a predetermined level is indicative of anegative responsiveness to cancer treatment.

According to this aspect of the present invention, the number of TILshaving a CD8⁺CD28⁻CD152⁻ signature is less than about 25%, morepreferably less than about 35% and even more preferably less than about45%.

The present inventors have shown that a TIL sample comprising asignificant percentage of CD94 ⁺ bearing lymphocytes which alreadycomprises a significantly low percentage of CD8⁺CD28⁻CD152⁻ bearinglymphocytes is further indicative of a negative responsiveness tocancer.

According to this embodiment, the predetermined level of CD94⁺ bearinglymphocytes is typically greater than about 0.5%, more preferablygreater than about 0.6% and even more preferably greater than about0.7%.

Other T lymphocyte signatures which have been shown to be predictors ofeffective cancer treatment include CD56+, CD4+CD85−CD94−, CD8+CD33+CD69+and CD4+CD33−CD69+. Thus for example, when more than about 20% of theTILs in a sample comprise a CD56+ signature, this is indicative of a TILsample being effective for cancer treatment. When more than about 38% ofthe TILs in a sample comprise a CD4+CD85−CD94− signature, this isindicative of a TIL sample being non-effective for cancer treatment.When more than about 17% of the TILs in a sample comprise aCD8+CD33+CD69+ signature, this is indicative of a TIL sample beingeffective for cancer treatment. When more than about 10% of the TILs ina sample comprise a CD4+CD33−CD69+ signature, this is indicative of aTIL sample being non-effective for cancer treatment.

By determining reactivity of subpopulations of TILs in a TIL sample, thepresent inventors uncovered several markers which predicted a negativeresponsiveness to cancer treatment. Thus, by depleting a TIL sample ofthose TILs which express the markers associated with negativeresponsiveness (i.e., lack of responsiveness), also referred to hereinas “harmful markers” the present inventors postulated they should beable to increase the reactivity of the TIL sample. As shown in FIGS.4A-C a TIL sample depleted of lymphocytes bearing a CD4, CD152, CD28,CD85 and/or CD94 marker comprised an increased reactivity towardsautologous cancer cells.

Thus, according to still another aspect of the present invention, thereis provided a method of treating cancer in a subject in need thereof,the method comprising depleting lymphocytes from a sample of TILs of thesubject, wherein the lymphocytes express CD4, CD152 and/or CD28.

As used herein, the term “treating” includes abrogating, substantiallyinhibiting, slowing or reversing the progression of a condition,substantially ameliorating clinical or aesthetical symptoms of acondition or substantially preventing the appearance of clinical oraesthetical symptoms of a condition.

As used herein, the phrase “subject in need thereof” refers to a subjectwhich has the disease. The subject may be a mammal, e.g. a human. Forexample if the disease being treated is melanoma, the subject istypically one being diagnosed with melanoma, with or without metastasis,at any stage of the disease (e.g. IA, IB, IIA, IIB, IIC, IIIA, IIIB,IIIC or IV).

The term “depleting” as used herein refers to a procedure thatsubstantially removes the indicated T lymphocyte population from the TILsample without also substantially removing the “effective” lymphocytesfrom the composition—i.e. those capable of destroying the tumor—e.g. thesubpopulation having a CD8⁺CD28⁻CD152⁻ signature.

The term “substantially removes” with respect to depletion of each ofthe cell types is intended to mean removal of at least 50% or more ofthe particular cell type, such as at least about 75%, about 80%, about90%, about 95%, or about 97%, including at least 99%, 99.5%, 99.9% ormore of the particular cell type.

Thus, by depleting lymphocytes express CD4, CD152 and/or CD28 from a TILsample, the remaining cells are substantially enriched for T lymphocytescomprising an “effective” lymphocyte population such as those comprisinga CD8⁺CD28⁻CD152⁻ signature.

According to one embodiment, depleting lymphocytes expressing the abovementioned markers may be effected by affinity labeling followed by labelbased separation. Thus, a fluorescently labeled anti-CD4, anti-CD152 oranti-CD28 antibody which specifically binds the “harmful” T-lymphocytesubpopulation (i.e. those T lymphocytes which deter the “effective” Tlymphocytes from destroying a tumor) may be used to separate the“harmful” T lymphocytes from the “effective” T lymphocytes.

According to still further features in the described preferredembodiments, depletion of T-lymphocytes expressing the above mentionedmarkers may be effected by affinity purification.

For example, a substrate including an antibody or a ligand capable ofspecifically binding CD4, CD152 and/or CD28, can be used to effectivelydeplete the “harmful” T-lymphocytes from the TIL sample.

The affinity substrate according to the present invention can be acolumn matrix such as, for example agarose, cellulose and the like, orbeads such as, for example, magnetic beads onto which the antibodiesdescribed above, are immobilized.

Thus, according to this aspect of the present invention, depletion ofT-lymphocytes expressing CD4, CD152 and/or CD28, can be effected viacolumn chromatography or magnetic bead separation.

It will be appreciated that the TIL sample may be depleted of othersubpopulations of T lymphocytes including for example those that expressCD85 and/or CD94.

As mentioned above, depletion of “harmful” T lymphocyte populations fromthe TIL sample effectively enriches for a T lymphocyte population whichis effective at destroying the tumor.

Thus, according to another aspect of this invention, there is provided amethod of treating cancer in a subject in need thereof, the methodcomprising enriching for a subpopulation of lymphocytes from a sample ofTILs of the subject, the subpopulation expressing a CD8⁺CD28⁻CD152⁻signature.

As used herein, the term “enriching” refers to a procedure which allowsthe TIL composition to comprise at least about 50%, preferably at leastabout 70%, more preferably at least about 80%, about 95%, about 97%,about 99% or more T lymphocytes comprising the CD8⁺CD28⁻CD152⁻signature.

The enriching may be effected using known cell sorting procedures suchas by using a fluorescence-activated cell sorter (FACS).

It will be appreciated that the enriching may also be effected bydepleting of non-relevant subpopulations as further described hereinabove.

The TIL population may also be enriched for other subpopulations (e.g. asubpopulation that expresses a CD4+CD33−CD69+ signature) in order tofurther enhance reactivity against tumors.

Following enrichment of a TIL sample for a particular subpopulation oflymphocytes (or depletion of a TIL sample of a particular subpopulationof lymphocytes e.g. CD8⁺CD69⁺CD33⁻ or CD69⁺), the lymphocytes aretypically expanded ex-vivo and re-injected back into the patientfollowing leuko-depletion.

Expansion of T-cell cultures can be accomplished by any of a number ofmethods as are known in the arts. For example, T cells may be expandedutilizing non-specific T-cell receptor stimulation in the presence offeeder lymphocytes and either IL-2 or IL-15. The non-specific T-cellreceptor stimulus can consist of around 30 ng/ml of OKT3, a mousemonoclonal anti-CD3 antibody available from Ortho, Raritan, N.J.

The autologous T-cells may be modified to express a T-cell growth factorthat promotes the growth and activation thereof. Any suitable methods ofmodification may be used. See, e.g., Sambrook and Russell, MolecularCloning, 3^(rd) ed., SCHL Press (2001). Desirably, modified autologousT-cells express the T-cell growth factor at high levels. T-cell growthfactor coding sequences, such as that of IL-2, are readily available inthe art, as are promoters, the operable linkage of which to a T-cellgrowth factor coding sequence promote high-level expression.

The T-cells can be administered by any suitable route as known in theart. For example, the T-cells may be administered as an intra-arterialor intravenous infusion, which preferably lasts approximately 30-60minutes. Other examples of routes of administration includeintraperitoneal, intrathecal and intralymphatic.

A suitable dose of T-cells to be administered is from about 2.3×10¹⁰T-cells to about 13.7×10¹⁰ T-cells.

According to one embodiment, the T cells are administered to the subjecttogether with a T-cell growth factor. The T-cell growth factor can beany suitable growth factor that promotes the growth and activation ofthe autologous T-cells administered. Examples of suitable T-cell growthfactors include IL-2, IL-7 and IL-15, which can be used alone or invarious combinations, such as IL-2 and IL-7, IL-2 and IL-15, IL-7 andIL-15, or IL-2, IL-7 and IL-15. IL-2 is available from Chiron, Emerwlle,Calif., whereas IL-7 is available from Cytheris, Vanves, Frances. IL-15can be obtained from PeproTech, Inc., Rocky Hill, N.J.

The T-cell growth factor can be administered by any suitable route. Ifmore than one T-cell growth factor is administered, they can beadministered simultaneously or sequentially, in any order, and by thesame route or different routes. According to one embodiment, the T-cellgrowth factor, such as IL-2, is administered intravenously as a bolusinjection. A typical dosage of IL-2 is about 720,000 IU/kg, administeredthree times daily until tolerance.

The nonmyeloablative lymphodepleting chemotherapy can be any suitablesuch therapy, which can be administered by any suitable route. Thenonmyeloablative lymphodepleting chemotherapy can comprise theadministration of cyclophosphamide and fludarabine, particularly if thecancer is melanoma. A preferred route of administering cyclophosphamideand fludarabine is intravenously. Likewise, any suitable dose ofcyclophosphamide and fludarabine can be administered. For melanom,typically around 60 mg/kg of cyclophosphamide are administered for twodays after which around 25 mg/m² fludarabine are administered for fivedays.

As used herein the term “about” refers to ±10%.

The terms “comprises”, “comprising”, “includes”, “including”, “having”and their conjugates mean “including but not limited to”.

The term “consisting of means “including and limited to”.

The term “consisting essentially of” means that the composition, methodor structure may include additional ingredients, steps and/or parts, butonly if the additional ingredients, steps and/or parts do not materiallyalter the basic and novel characteristics of the claimed composition,method or structure.

As used herein, the singular form “a”, “an” and “the” include pluralreferences unless the context clearly dictates otherwise. For example,the term “a compound” or “at least one compound” may include a pluralityof compounds, including mixtures thereof.

Throughout this application, various embodiments of this invention maybe presented in a range format. It should be understood that thedescription in range format is merely for convenience and brevity andshould not be construed as an inflexible limitation on the scope of theinvention. Accordingly, the description of a range should be consideredto have specifically disclosed all the possible subranges as well asindividual numerical values within that range. For example, descriptionof a range such as from 1 to 6 should be considered to have specificallydisclosed subranges such as from 1 to 3, from 1 to 4, from 1 to 5, from2 to 4, from 2 to 6, from 3 to 6 etc., as well as individual numberswithin that range, for example, 1, 2, 3, 4, 5, and 6. This appliesregardless of the breadth of the range.

Whenever a numerical range is indicated herein, it is meant to includeany cited numeral (fractional or integral) within the indicated range.The phrases “ranging/ranges between” a first indicate number and asecond indicate number and “ranging/ranges from” a first indicate number“to” a second indicate number are used herein interchangeably and aremeant to include the first and second indicated numbers and all thefractional and integral numerals therebetween.

As used herein the term “method” refers to manners, means, techniquesand procedures for accomplishing a given task including, but not limitedto, those manners, means, techniques and procedures either known to, orreadily developed from known manners, means, techniques and proceduresby practitioners of the chemical, pharmacological, biological,biochemical and medical arts.

It is appreciated that certain features of the invention, which are, forclarity, described in the context of separate embodiments, may also beprovided in combination in a single embodiment. Conversely, variousfeatures of the invention, which are, for brevity, described in thecontext of a single embodiment, may also be provided separately or inany suitable sub-combination or as suitable in any other describedembodiment of the invention. Certain features described in the contextof various embodiments are not to be considered essential features ofthose embodiments, unless the embodiment is inoperative without thoseelements.

Various embodiments and aspects of the present invention as delineatedhereinabove and as claimed in the claims section below find experimentalsupport in the following examples.

Examples

Reference is now made to the following examples, which together with theabove descriptions, illustrate the invention in a non limiting fashion.

Generally, the nomenclature used herein and the laboratory proceduresutilized in the present invention include molecular, biochemical,microbiological and recombinant DNA techniques. Such techniques arethoroughly explained in the literature. See, for example, “MolecularCloning: A laboratory Manual” Sambrook et al., (1989); “CurrentProtocols in Molecular Biology” Volumes I-III Ausubel, R. M., ed.(1994); Ausubel et al., “Current Protocols in Molecular Biology”, JohnWiley and Sons, Baltimore, Md. (1989); Perbal, “A Practical Guide toMolecular Cloning”, John Wiley & Sons, New York (1988); Watson et al.,“Recombinant DNA”, Scientific American Books, New York; Birren et al.(eds) “Genome Analysis: A Laboratory Manual Series”, Vols. 1-4, ColdSpring Harbor Laboratory Press, New York (1998); methodologies as setforth in U.S. Pat. Nos. 4,666,828; 4,683,202; 4,801,531; 5,192,659 and5,272,057; “Cell Biology: A Laboratory Handbook”, Volumes I-III Cellis,J. E., ed. (1994); “Culture of Animal Cells—A Manual of Basic Technique”by Freshney, Wiley-Liss, N.Y. (1994), Third Edition; “Current Protocolsin Immunology” Volumes I-III Coligan J. E., ed. (1994); Stites et al.(eds), “Basic and Clinical Immunology” (8^(th) Edition), Appleton &Lange, Norwalk, Conn. (1994); Mishell and Shiigi (eds), “SelectedMethods in Cellular Immunology”, W. H. Freeman and Co., New York (1980);available immunoassays are extensively described in the patent andscientific literature, see, for example, U.S. Pat. Nos. 3,791,932;3,839,153; 3,850,752; 3,850,578; 3,853,987; 3,867,517; 3,879,262;3,901,654; 3,935,074; 3,984,533; 3,996,345; 4,034,074; 4,098,876;4,879,219; 5,011,771 and 5,281,521; “Oligonucleotide Synthesis” Gait, M.J., ed. (1984); “Nucleic Acid Hybridization” Hames, B. D., and HigginsS. J., eds. (1985); “Transcription and Translation” Hames, B. D., andHiggins S. J., eds. (1984); “Animal Cell Culture” Freshney, R. I., ed.(1986); “Immobilized Cells and Enzymes” IRL Press, (1986); “A PracticalGuide to Molecular Cloning” Perbal, B., (1984) and “Methods inEnzymology” Vol. 1-317, Academic Press; “PCR Protocols: A Guide ToMethods And Applications”, Academic Press, San Diego, Calif. (1990);Marshak et al., “Strategies for Protein Purification andCharacterization—A Laboratory Course Manual” CSHL Press (1996); all ofwhich are incorpotaed by reference as if fully set forth herein. Othergeneral references are provided throughout this document. The procedurestherein are believed to be well known in the art and are provided forthe convenience of the reader. All the information contained therein isincorporated herein by reference.

Example 1 Determination of Immune Subpopulation Composition of IFN-γSecreting TILs (Tumor Infiltrating Lymphocytes)

Materials and Methods

Measurement of IFN-γ secretion: IFN-γ secretion was measured afterco-incubation of 10⁵ TIL cells with 10⁵ viable autologous melanoma cellsfor an overnight period. The amount of IFN-γ secretion in the culturesupernatant was detected using standard sandwich ELISA protocol.

Flow Cytometry: The markers used for subpopulation mapping includedcombinations of triple staining from the pool of the following surfacereceptors: CD3, CD4, CD8, CD25, CD28, CD33, CD56, CD69, CD85, CD94,CD152 (FIG. 1B) and the intracellular cytotoxic proteins perforin andgranzyme B.

The following antibodies (Abs) were purchased from DakoCytomation: CD4,CD25, CD28, CD56, CD69, CD85, CD94, CD152. The following Abs werepurchased from BD Pharmingen: CD3, CD8, CD33. Perforin and Granzyme Bantibodies were purchased from eBioscience.

For flow cytometric analysis of cell surface, 2.5×10⁵ cells were washedand resuspended in PBS containing 0.1% BSA. Cells were incubated on icewith the appropriate antibody for 20 min and then washed. Samples wereanalyzed on a FACScaliber (BD Biosciences, Mountain View, Calif.).Background staining was assessed by use of an isotype control antibody.

Results

As a first step, the reactivity of 91 TILS from 26 melanoma patients wasdetermined by measuring IFN-γ secretion following co-incubation of theTILs with autologous melanoma. Using the clinical threshold of 200 pg/mlIFN-γ, 39 TIL cultures were determined as reactive and 52 as nonreactive(Different TILs from the same patient produced different reactivitylevels). The immune subpopulation compositions of these cultures werecharacterized by multicolor flow cytometry. Each triple staining ofthree different receptors X, Y and Z resulted in 6 single staining (X⁺,X⁻, Y⁺, Y⁻, Z⁺, Z⁻), 12 double staining (e.g. X⁺Y) and 8 triple staining(e.g. X⁺Y⁺Z). The single, double and triple staining produces ahierarchy of subpopulation characterization ranging from general to morespecific subpopulations. A quality control filtering procedure wasemployed yielding a final dataset containing 33 distinct subpopulations(see FIGS. 5-9 for filtering and dataset description).

Example 2 Comparison Between Individual Subpopulation Fractions and UseThereof to Predict Reactive and Nonreactive TILs

Materials and Methods

SVM classification: SVM classifications were performed with thegist-train-svm software www.bioinformatics.ubc.ca/gist/. Allclassifications were performed with a linear kernel and input data wasnormalized by rescaling the columns to values between −1 and 1. Alltests were conducted by applying a ‘leave three out’ procedure. SVMperformance was evaluated by the ROC (receive operating characteristics)analysis which calculates the true positive rate versus True negativerate for different cutoffs. The ROC value namely the area under A ROCcurve was reported for each test. In addition, the total accuracy (TA),sensitivity (SN), specificity (SP) and the Matthews correlationcoefficient (CC) were calculated.

The present inventors compared individual subpopulation fractions andused them to predict reactive and nonreactive TILs (FIG. 2). Theclassification accuracy yielded a Matthews correlation coefficients(MCC) ranging from 0 to 0.58 and total accuracy ranging from 40% to 78%.In general, the discriminative power of individual subpopulationscharacterized by triple staining was superior to that of a single anddouble staining, which may be attributed to the better characterizationof identity or functional state of the first compared to the later. Forexample, a CD8⁺ marker is an indicator of cytotoxic activity whileCD8⁺CD28⁻CD152⁻ is, in addition to being cytotoxic, also fully activatedand bearing no CD152 inhibitory receptors. This analysis emphasizes thelimited predictive power of individual subpopulations.

To examine whether the combination of multiple subpopulations improvesthe prediction accuracy a support vector machine (SVM) model [W. S.Noble, Nat. Biotechnol. 24, 1565 (2006)] was applied.

Predicting TIL reactivity using an SVM model: Briefly, each TIL wasmapped to a point in a multi-dimensional space according to itssubpopulation constituents. The SVM classifier generates a hyper-surfacethat separates instances of the two classes. All classifications weredone with a linear kernel and input data was normalized by rescaling thecolumns to values between −1 and 1. The classification was tested byapplying a ‘leave one out’ procedure. SVM performance was evaluatedusing the Matthews correlation coefficient (MCC):

$\begin{matrix}{{MCC} = \frac{{{TP} \cdot {TN}} - {{FP} \cdot {FN}}}{\sqrt{\left( {{TP} + {FN}} \right)\left( {{TP} + {FP}} \right)\left( {{TN} + {FN}} \right)\left( {{TN} + {FP}} \right)}}} & {{formula}\mspace{14mu} I}\end{matrix}$

where TP, FP, TN, FN are true positives, false positives, true negativesand false negatives respectively. The total accuracy (TA), sensitivity(SN), specificity (SP) and the ROC (receiver operating characteristics)values were also used. To optimize SVM classification a recursivefeature elimination procedure was used. In each iteration the 10% of theleast predictive features were removed, as determined by the errorbound. Four different training sets were analyzed: single, double andtriple staining features and the filtered training set (see FIG. 9). Asa quality check d an additional run of the filtered training set wasperformed in which a group of 10 random features was included. Thefeature elimination rate of the subpopulation features was slowercompared to the random features indicating that the former areinformative of TIL reactivity.

The final SVM classifier contained the minimal feature subset thatdisplayed the maximal MCC value. The optimal SVM with MCC=0.74 had eightparameters: CD69⁺, CD4⁺CD69⁻CD33⁺, CD8⁺CD28⁻CD152⁻, CD8⁺CD85⁻CD94⁻,CD8⁺CD69⁻CD33⁺, CD8⁺CD69⁺CD33⁻, CD8⁺CD69⁻CD33⁻, CD8⁺CD69⁺CD33⁻.

Some of the TIL samples in this study belong to a same patient. Toexclude the possibility of interdependences between the samples that maycause a bias in the prediction a Bootstrapping control was performed. Aleave five out procedure, 10,000 itarations was performed. The resultswere similar to the SVM testing. SVM classifications were performed withthe gist software www.bioinformatics.ubc.ca/gist/.

In summary, the prediction accuracy of the SVM model was MCC=0.74 (87%total accuracy) compared to an MCC=0.58 (total accuracy 78%) achieved bythe best individual subpopulation.

These results demonstrate the advantage of combining differentsubpopulation fractions for reactivity prediction and are in accordancewith the “multi-player” nature of the immune system. The SVM had 13%misclassifications that may be explained by flow cytometry sensitivitylimitations, important subpopulations that were not measured and theinherent stochasticity of the system. The fact that a high accuracy ofprediction can be achieved by the SVM indicates that there is anunderlying pattern connecting between the subpopulation fractions andthe ultimate TIL reactivity.

Example 3 Use of Subpopulation Signatures to Predict TIL Reactivity

Results

Since the SVM model does not lend itself easily to biologicalinterpretation, the present inventors decided to investigate theunderlying biological rational that governs TIL reactivity. The usage ofdifferential expression signatures has become a well established methodfor distinguishing between various cellular states and differentpathological conditions. This concept was applied to cell populations,by using a similar notion of “subpopulations signature” that can be usedto differentiate between reactive and nonreactive TILs (see FIG. 3A andFIG. 11). Each column corresponds to a TIL culture and the rowsrepresent subpopulations. Two significant clusters emerge, eachrepresenting a profile of CD4⁺ and CD8⁺ enriched subsets. These twomarkers represent regulatory and cytotoxic T-cell subpopulationsrespectively (FIG. 1B). Interestingly, the two clusters also separatebetween nonreactive and reactive TILs (Fischer exact P<10⁻³). Thissuggests that TIL reactivity against melanoma is largely dictated by itssubpopulation composition. It was also observed that the nonreactivecluster is further divided into two sub-clusters, both of which areenriched with nonreactive TILs that have distinct profiles. The first ismostly CD4⁺ while the other is a mixture of CD8⁺ and CD4⁺ subpopulationderivatives, suggesting CD4⁺ dominance over CD8⁺. To further simplifythe subpopulation signature a decision tree algorithm was used thatproduced a simple set of rules for distinguishing between reactive andnonreactive TILs (FIG. 3B). The accuracy of these rule based predictionsare 89% with MCC=0.79. These rules highlight three subpopulations,namely: CD8⁺CD28⁻CD152⁻, CD94⁻ and CD8⁺CD69⁺CD33. The first emphasizesthe role of the CD28 and 152 receptors in determining the TIL reactivityin addition to CD8⁺. Specifically, the present observation that reactiveCD8⁺ T-cells lack both co-stimulatory CD28 receptor and theco-inhibitory receptor CD152 on their surface is in agreement withcurrent knowledge. CD28 tend to become down regulated and internalizedfollowing proper T-cell activation [S. C. Eck, D. Chang, A. D. Wells, L.A. Turka, Transplantation 64, 1497 (1997); P. S. Linsley, J. Bradshaw,M. Umes, L. Grosmaire, J. A. Ledbetter, J. Immunol. 150, 3161 (1993)].The absence of CD152 receptor on reactive TILs is in accordance with itspotent co-inhibitory role [M. L. Alegre, K. A. Frauwirth, C. B.Thompson, Nat. Rev. Immunol. 1, 220 (2001)]. The second subpopulation ismarked by CD94⁺, an inhibitory receptor expressed in low levels onT-cells [P. J. Leibson, Curr. Opin. Immunol. 16, 328 (2004)]. Itsinhibitory function may explain why higher levels of it are correlatedwith nonreactive TILs. The third subpopulation (CD8⁺CD69⁺CD33⁻) ischaracterized by the CD69 ⁺ and CD33⁻ receptor staining Little is knownabout the function of these two receptors. The present findings suggestthat this subpopulation has a yet unknown role in determining T-cellfunctionality.

Example 4 Use of Subpopulation Analysis to Predict the Exact Level ofIFN-γ Secretion

To test whether subpopulation analysis can be used, not only to classifybetween reactive and nonreactive TILs, but also to predict the exactlevel of IFN-γ secretion, attention was focused exclusively on thereactive TILs. To this end a linear regression was performed on pairs ofsubpopulations and IFN-γ levels. By using an equation of the formIFN-γ=α+β₁·X₁+β₂·X₂ where X₁ and X₂ represent the fraction of twodifferent subpopulations it was possible to accurately determine theexact levels of IFN-γ with P<10⁻⁴ (see FIG. 3C). The pair that yieldedoptimal results, in terms of IFN-γ secretion was CD8⁺CD28⁻ andCD8⁺CD69⁺CD33⁻. Notably, these subpopulations are similar to those usedfor classification between reactive and nonreactive TILs in the decisiontree (FIG. 3B).

Overall, these results indicate that TIL anti-tumor reactivity is toocomplex to be explained by an individual subpopulation or receptor. Yet,the combination of a few subpopulations based rules and simple formulascan explain the reactivity to a large extent.

Example 5 Controlling the Reactivity of TILs by Manipulation of theirSubpopulation Fractions

These observations raise the conjecture whether one could controlreactivity of TILs by manipulating their subpopulation fractions. Totest this hypothesis nonreactive associated subpopulations wereselectively depleted.

Materials and Methods

T cell depletion was performed by incubating the TILs with anti-CD4and/or anti-CD28 and/or anti-CD152 and/or anti-CD85 and/or anti-CD94 for20 minutes. Subsequently, cells were mixed with anti mouse IgG coatedmagnetic beads (Dynal, Lake Success, N.Y.) for an additional 10 minutes,followed by magnetic depletion for 5 minutes. The negative fraction wasthen washed 3 times with cold PBS 0.1% BSA and was incubated for 36hours at 37° C.

Results

The receptors used for depleting these subpopulations were CD4, CD28,CD85, CD94 and CD152. The experiments were performed on 12 nonreactivefresh TIL cultures that originated from four different melanoma patients(Table 6, herein below) and were not part of the 91 TIL samples used forthe subpopulation signature elucidation. Reactivity levels, in terms ofIFN-γ secretion, were measured. TILs with IFN-γ levels that exceeded 200pg/ml were determined as reactive and otherwise as nonreactive (markedwith a ‘+’ and ‘−’ respectively).

TABLE 6 Reactive after Patient TIL separation 1 1 + 2 − 2 1 + 2 − 3 +4 + 5 − 3 1 + 4 1 + 2 + 3 + 4 +

First the subpopulation frequencies of each TIL were determined. Then,the inhibitory related subpopulations were depleted using magnetic beadnegative selection. After 36 hours of recovery both original andmanipulated TILs were challenged with autologous melanoma for 12 hoursfollowed by supernatant IFN-γ measurement. Remarkably, 9 of the 12originally nonreactive TILs became reactive after manipulation (FIG.4A). The IFN-γ level of the 9 reactive TILs exceeded the 200 pg/mlclinical threshold with levels ranging between 300-4000 pg/ml (a 1.5 to20 fold increase above the threshold). Two of the three TILs thatretained a nonreactive state after manipulation also exhibited anincrease in IFN-γ levels. As a negative control specificity andspontaneous release of IFN-γ secretion was tested by incubating the TILswith unrelated melanoma or culture media. In all controls IFN-γ levelsremained below threshold indicating specificity and low spontaneousrelease (see FIG. 4A).

The fact that nonreactive TILs could be transformed into reactive onessuggests that nonreactivity is largely dictated by simple subpopulationinteractions rather than lack of specificity to melanoma cancerepitopes.

In order to link the change in reactivity with the change in theunderlying subpopulation composition, TIL profiles were examined priorand after the manipulation (see FIG. 4B). For this analysis 10 of the 12TILs were used that had sufficient cell counts. The profile of 9 of theremaining 10 TILs prior to manipulation was similar to that of thenonreactive TILs as determined by the original 91 sample dataset(compare FIG. 3A and FIG. 4B). It can be seen that the shift fromnonreactive to reactive state is accompanied by a transformation ofsubpopulation signature as indicated by blue arrows in FIG. 4B. Thisshift in profiles is further illustrated in FIG. 4C.

Understanding and predicting the output of a heterogeneous cellpopulation is a highly challenging task with many biological andclinical implications. In this study multi-parametric modeling was usedthat is based on subpopulation fractions in order to accurately predictthe reactivity levels of TILs, an example of an immune heterogeneouscell population. The present results show that although the number ofpossible subpopulation combinations is infinite, in practice TILs fallinto a few distinct profiles, which may be defined as “subpopulationssignatures”. These findings were further simplified into a set of rulesthat map between subpopulation proportions and TIL reactivity. Guided bythese rules specific subpopulations were selected for enrichment anddepletion and the present inventors were able to transform nonreactiveTILs into reactive ones. This approach may be applied in order tooptimize the ACT clinical protocol by studying and manipulating TILs inthe context of an objective clinical response. This general frame workdemonstrates the practical implications of systems biology in thecontext of clinical research and can be further extended to predict,understand and control cell population functions in fields such as stemcells, tumor immunology and tissue engineering.

1. A method of determining responsiveness to cancer treatment in asubject in need thereof, the method comprising analyzing a frequency oftumor infiltrating lymphocytes (TILs) having a CD8+CD28−CD152− signaturein a sample of the subject, wherein a frequency of TILs having saidCD8+CD28−CD152− signature above a predetermined level is indicative of apositive responsiveness to cancer treatment.
 2. The method of claim 1,further comprising analyzing a frequency of TILs having a CD8+CD69+CD33−signature in the TIL sample, wherein a frequency of TILs having saidCD8+CD69+CD33− signature and said CD8+CD28−CD152− signature above apredetermined level is indicative of a negative responsiveness to cancertreatment.
 3. A method of determining responsiveness to cancer treatmentin a subject in need thereof, the method comprising analyzing afrequency of TILs having a CD8+CD28−CD152− signature in a sample of thesubject, wherein a frequency of TILs having a CD8+CD28−CD152− signaturebelow a predetermined level is indicative of a negative responsivenessto cancer treatment.
 4. The method of claim 3, further comprisinganalyzing a frequency of TILs having a CD94+ signature in the sample,wherein a frequency of TILs not having said CD8+CD28−CD152− signaturewhilst having a CD94+ signature above a predetermined level is furtherindicative of a negative responsiveness to cancer treatment.
 5. A methodof predicting T cell responsiveness to a cancer in a subject, comprisinganalyzing subpopulation marker signatures in a TIL sample of thesubject, wherein a subpopulation marker signature corresponding to areactive marker signatures as defined by FIG. 3A is indicative of T cellresponsiveness and a subpopulation marker signature corresponding to anon-reactive marker signature as defined by FIG. 3A is indicative of anon T cell responsiveness.
 6. The method of claim 1, wherein said cancertreatment comprises adoptive transfer therapy.
 7. A method of treatingcancer in a subject in need thereof, the method comprising depletinglymphocytes from a sample of TILs of the subject, wherein saidlymphocytes express CD4, CD152 and CD28.
 8. The method of claim 7,further comprising depleting additional lymphocytes of the subjectwherein said additional lymphocytes express CD85 and/or CD94.
 9. Amethod of treating cancer in a subject in need thereof, the methodcomprising enriching for a subpopulation of lymphocytes from a sample ofTILs of the subject, said subpopulation expressing a CD8+CD28−CD152−signature.
 10. The method of claim 9, further comprising depleting anadditional subpopulation of lymphocytes from said sample ofCD8+CD28−CD152− enriched TILs, said additional subpopulation expressinga CD8+CD69+CD33− signature.
 11. The method of claim 1, wherein thesubject has a cancer selected from the group consisting of prostatecancer, renal cell carcinoma, glioma and melanoma.
 12. A method ofdetermining a reactivity of a subpopulation of TILs in a TIL sample, themethod comprising: (a) assaying an activity of a statisticallysignificant number of TIL samples; (b) analyzing said TIL samples byflow cytometry analysis of at least three markers per cell in order toclassify subpopulations of cells, wherein at least one of said threemarkers is CD4 or CD8, at least a second of said three markers is acytokine or chemokine and at least a third of said three markers is anadhesion molecule, a co-inhibitory receptor, a co-stimulatory receptoror a protein set forth in Table 5; and (c) analyzing a frequency of atleast one subpopulation in the TIL sample, wherein a frequency above apredetermined threshold indicates that said at least one subpopulationof cells is associated with said activity.
 13. The method of claim 12,further comprising removing said subpopulations following said analyzingsaid frequency, wherein a subpopulation comprising a frequency lowerthan 1% is removed.
 14. The method of claim 3, wherein said cancertreatment comprises adoptive transfer therapy.
 15. The method of claim3, wherein the subject has a cancer selected from the group consistingof prostate cancer, renal cell carcinoma, glioma and melanoma.
 16. Themethod of claim 5, wherein the subject has a cancer selected from thegroup consisting of prostate cancer, renal cell carcinoma, glioma andmelanoma.
 17. The method of claim 7, wherein the subject has a cancerselected from the group consisting of prostate cancer, renal cellcarcinoma, glioma and melanoma.
 18. The method of claim 9, wherein thesubject has a cancer selected from the group consisting of prostatecancer, renal cell carcinoma, glioma and melanoma.